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Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Rüdiger Frey

    (Vienna University of Economics and Business, Institute for Statistics and Mathematics)

  • Verena Köck

    (Vienna University of Economics and Business, Institute for Statistics and Mathematics)

Abstract

In recent years a large literature on deep learning based methods for the numerical solution partial differential equations has emerged; results for integro-differential equations on the other hand are scarce. In this short paper we study deep neural network algorithms for solving linear parabolic partial integro-differential equations with boundary conditions in high dimension. To show the viability of our approach we discuss a test case study from insurance.

Suggested Citation

  • Rüdiger Frey & Verena Köck, 2022. "Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics," Springer Books, in: Marco Corazza & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 272-277, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-99638-3_44
    DOI: 10.1007/978-3-030-99638-3_44
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